Overview

Dataset statistics

Number of variables45
Number of observations240000
Missing cells8890408
Missing cells (%)82.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.2 MiB
Average record size in memory368.0 B

Variable types

Categorical25
Numeric12
Unsupported8

Warnings

placement_support_banner_tab_clicked has constant value "1.0" Constant
programme_curriculum has constant value "1.0" Constant
programme_faculty has constant value "1.0" Constant
request_callback_on_instant_customer_support_cta_clicked has constant value "1.0" Constant
created_date has a high cardinality: 234753 distinct values High cardinality
city_mapped has a high cardinality: 4597 distinct values High cardinality
first_utm_source_c has a high cardinality: 142 distinct values High cardinality
total_leads_droppped is highly correlated with call_us_button_clicked and 6 other fieldsHigh correlation
call_us_button_clicked is highly correlated with total_leads_droppped and 4 other fieldsHigh correlation
career_assistance is highly correlated with careers and 4 other fieldsHigh correlation
career_impact is highly correlated with careers and 8 other fieldsHigh correlation
careers is highly correlated with career_assistance and 5 other fieldsHigh correlation
companies is highly correlated with total_leads_droppped and 12 other fieldsHigh correlation
download_button_clicked is highly correlated with total_leads_droppped and 8 other fieldsHigh correlation
emi_partner_click is highly correlated with total_leads_droppped and 12 other fieldsHigh correlation
emi_plans_clicked is highly correlated with total_leads_droppped and 12 other fieldsHigh correlation
fee_component_click is highly correlated with download_button_clicked and 3 other fieldsHigh correlation
hiring_partners is highly correlated with total_leads_droppped and 3 other fieldsHigh correlation
live_chat_button_clicked is highly correlated with careers and 1 other fieldsHigh correlation
placement_support is highly correlated with emi_partner_click and 2 other fieldsHigh correlation
shorts_entry_click is highly correlated with emi_partner_clickHigh correlation
social_referral_click is highly correlated with whatsapp_chat_clickHigh correlation
specialisation_tab_clicked is highly correlated with companies and 2 other fieldsHigh correlation
specializations is highly correlated with download_button_clicked and 2 other fieldsHigh correlation
specilization_click is highly correlated with download_button_clicked and 4 other fieldsHigh correlation
syllabus is highly correlated with career_impact and 3 other fieldsHigh correlation
syllabus_expand is highly correlated with call_us_button_clicked and 11 other fieldsHigh correlation
syllabus_submodule_expand is highly correlated with companies and 8 other fieldsHigh correlation
tab_career_assistance is highly correlated with career_impact and 7 other fieldsHigh correlation
tab_job_opportunities is highly correlated with career_impact and 6 other fieldsHigh correlation
tab_student_support is highly correlated with career_assistance and 4 other fieldsHigh correlation
view_programs_page is highly correlated with emi_plans_clickedHigh correlation
whatsapp_chat_click is highly correlated with total_leads_droppped and 9 other fieldsHigh correlation
app_complete_flag is highly correlated with call_us_button_clicked and 3 other fieldsHigh correlation
total_leads_droppped is highly correlated with call_us_button_clicked and 6 other fieldsHigh correlation
referred_lead is highly correlated with social_referral_click and 1 other fieldsHigh correlation
call_us_button_clicked is highly correlated with total_leads_droppped and 4 other fieldsHigh correlation
career_assistance is highly correlated with careers and 5 other fieldsHigh correlation
career_impact is highly correlated with careers and 8 other fieldsHigh correlation
careers is highly correlated with career_assistance and 3 other fieldsHigh correlation
companies is highly correlated with total_leads_droppped and 12 other fieldsHigh correlation
download_button_clicked is highly correlated with total_leads_droppped and 10 other fieldsHigh correlation
emi_partner_click is highly correlated with total_leads_droppped and 16 other fieldsHigh correlation
emi_plans_clicked is highly correlated with total_leads_droppped and 12 other fieldsHigh correlation
fee_component_click is highly correlated with download_button_clicked and 4 other fieldsHigh correlation
hiring_partners is highly correlated with career_assistance and 3 other fieldsHigh correlation
live_chat_button_clicked is highly correlated with careers and 3 other fieldsHigh correlation
placement_support is highly correlated with emi_partner_click and 2 other fieldsHigh correlation
shorts_entry_click is highly correlated with emi_partner_click and 1 other fieldsHigh correlation
social_referral_click is highly correlated with referred_lead and 1 other fieldsHigh correlation
specialisation_tab_clicked is highly correlated with companies and 8 other fieldsHigh correlation
specializations is highly correlated with download_button_clicked and 6 other fieldsHigh correlation
specilization_click is highly correlated with total_leads_droppped and 5 other fieldsHigh correlation
syllabus is highly correlated with career_assistance and 4 other fieldsHigh correlation
syllabus_expand is highly correlated with total_leads_droppped and 14 other fieldsHigh correlation
syllabus_submodule_expand is highly correlated with companies and 9 other fieldsHigh correlation
tab_career_assistance is highly correlated with career_assistance and 8 other fieldsHigh correlation
tab_job_opportunities is highly correlated with career_impact and 6 other fieldsHigh correlation
tab_student_support is highly correlated with career_assistance and 2 other fieldsHigh correlation
view_programs_page is highly correlated with emi_partner_click and 1 other fieldsHigh correlation
whatsapp_chat_click is highly correlated with download_button_clicked and 9 other fieldsHigh correlation
app_complete_flag is highly correlated with call_us_button_clicked and 3 other fieldsHigh correlation
total_leads_droppped is highly correlated with call_us_button_clicked and 3 other fieldsHigh correlation
referred_lead is highly correlated with social_referral_clickHigh correlation
call_us_button_clicked is highly correlated with total_leads_droppped and 4 other fieldsHigh correlation
career_assistance is highly correlated with careers and 4 other fieldsHigh correlation
career_impact is highly correlated with careers and 8 other fieldsHigh correlation
careers is highly correlated with career_assistance and 3 other fieldsHigh correlation
companies is highly correlated with total_leads_droppped and 12 other fieldsHigh correlation
download_button_clicked is highly correlated with call_us_button_clicked and 5 other fieldsHigh correlation
emi_partner_click is highly correlated with total_leads_droppped and 9 other fieldsHigh correlation
emi_plans_clicked is highly correlated with total_leads_droppped and 6 other fieldsHigh correlation
fee_component_click is highly correlated with syllabus_submodule_expandHigh correlation
hiring_partners is highly correlated with career_assistance and 2 other fieldsHigh correlation
live_chat_button_clicked is highly correlated with careers and 1 other fieldsHigh correlation
placement_support is highly correlated with emi_partner_click and 1 other fieldsHigh correlation
shorts_entry_click is highly correlated with whatsapp_chat_clickHigh correlation
social_referral_click is highly correlated with referred_lead and 1 other fieldsHigh correlation
specialisation_tab_clicked is highly correlated with companies and 5 other fieldsHigh correlation
specializations is highly correlated with download_button_clicked and 3 other fieldsHigh correlation
specilization_click is highly correlated with download_button_clicked and 3 other fieldsHigh correlation
syllabus is highly correlated with career_assistance and 3 other fieldsHigh correlation
syllabus_expand is highly correlated with call_us_button_clicked and 9 other fieldsHigh correlation
syllabus_submodule_expand is highly correlated with companies and 4 other fieldsHigh correlation
tab_career_assistance is highly correlated with career_impact and 4 other fieldsHigh correlation
tab_job_opportunities is highly correlated with career_impact and 3 other fieldsHigh correlation
tab_student_support is highly correlated with career_assistance and 1 other fieldsHigh correlation
view_programs_page is highly correlated with emi_partner_click and 1 other fieldsHigh correlation
whatsapp_chat_click is highly correlated with emi_partner_click and 7 other fieldsHigh correlation
app_complete_flag is highly correlated with call_us_button_clicked and 2 other fieldsHigh correlation
tab_job_opportunities is highly correlated with emi_partner_click and 10 other fieldsHigh correlation
placement_support is highly correlated with first_utm_medium_cHigh correlation
first_utm_medium_c is highly correlated with placement_support and 8 other fieldsHigh correlation
emi_partner_click is highly correlated with tab_job_opportunities and 15 other fieldsHigh correlation
careers is highly correlated with tab_job_opportunities and 8 other fieldsHigh correlation
emi_plans_clicked is highly correlated with tab_job_opportunities and 13 other fieldsHigh correlation
app_complete_flag is highly correlated with first_platform_c and 1 other fieldsHigh correlation
specialisation_tab_clicked is highly correlated with emi_partner_click and 9 other fieldsHigh correlation
syllabus_expand is highly correlated with tab_job_opportunities and 13 other fieldsHigh correlation
syllabus_submodule_expand is highly correlated with tab_job_opportunities and 13 other fieldsHigh correlation
specializations is highly correlated with first_utm_medium_c and 6 other fieldsHigh correlation
tab_student_support is highly correlated with tab_job_opportunities and 9 other fieldsHigh correlation
tab_career_assistance is highly correlated with tab_job_opportunities and 8 other fieldsHigh correlation
hiring_partners is highly correlated with tab_job_opportunities and 5 other fieldsHigh correlation
live_chat_button_clicked is highly correlated with syllabus_submodule_expand and 2 other fieldsHigh correlation
first_platform_c is highly correlated with tab_job_opportunities and 9 other fieldsHigh correlation
career_impact is highly correlated with tab_job_opportunities and 7 other fieldsHigh correlation
syllabus is highly correlated with emi_partner_click and 7 other fieldsHigh correlation
whatsapp_chat_click is highly correlated with first_utm_medium_c and 8 other fieldsHigh correlation
total_leads_droppped is highly correlated with emi_partner_click and 12 other fieldsHigh correlation
download_button_clicked is highly correlated with emi_plans_clicked and 10 other fieldsHigh correlation
career_assistance is highly correlated with tab_job_opportunities and 13 other fieldsHigh correlation
specilization_click is highly correlated with first_utm_medium_c and 2 other fieldsHigh correlation
fee_component_click is highly correlated with first_utm_medium_c and 6 other fieldsHigh correlation
city_mapped has 9403 (3.9%) missing values Missing
1_on_1_industry_mentorship has 240000 (100.0%) missing values Missing
call_us_button_clicked has 239998 (> 99.9%) missing values Missing
career_assistance has 239996 (> 99.9%) missing values Missing
career_coach has 240000 (100.0%) missing values Missing
career_impact has 239995 (> 99.9%) missing values Missing
careers has 239980 (> 99.9%) missing values Missing
chat_clicked has 240000 (100.0%) missing values Missing
companies has 239998 (> 99.9%) missing values Missing
download_button_clicked has 239893 (> 99.9%) missing values Missing
download_syllabus has 240000 (100.0%) missing values Missing
emi_partner_click has 239921 (> 99.9%) missing values Missing
emi_plans_clicked has 239939 (> 99.9%) missing values Missing
fee_component_click has 239995 (> 99.9%) missing values Missing
hiring_partners has 239991 (> 99.9%) missing values Missing
homepage_upgrad_support_number_clicked has 240000 (100.0%) missing values Missing
industry_projects_case_studies has 240000 (100.0%) missing values Missing
live_chat_button_clicked has 239982 (> 99.9%) missing values Missing
payment_amount_toggle_mover has 240000 (100.0%) missing values Missing
placement_support has 239997 (> 99.9%) missing values Missing
placement_support_banner_tab_clicked has 239999 (> 99.9%) missing values Missing
program_structure has 240000 (100.0%) missing values Missing
programme_curriculum has 239999 (> 99.9%) missing values Missing
programme_faculty has 239999 (> 99.9%) missing values Missing
request_callback_on_instant_customer_support_cta_clicked has 239998 (> 99.9%) missing values Missing
shorts_entry_click has 239987 (> 99.9%) missing values Missing
social_referral_click has 239995 (> 99.9%) missing values Missing
specialisation_tab_clicked has 239956 (> 99.9%) missing values Missing
specializations has 239996 (> 99.9%) missing values Missing
specilization_click has 239993 (> 99.9%) missing values Missing
syllabus has 239971 (> 99.9%) missing values Missing
syllabus_expand has 239906 (> 99.9%) missing values Missing
syllabus_submodule_expand has 239958 (> 99.9%) missing values Missing
tab_career_assistance has 239975 (> 99.9%) missing values Missing
tab_job_opportunities has 239981 (> 99.9%) missing values Missing
tab_student_support has 239992 (> 99.9%) missing values Missing
view_programs_page has 239988 (> 99.9%) missing values Missing
whatsapp_chat_click has 239975 (> 99.9%) missing values Missing
created_date is uniformly distributed Uniform
call_us_button_clicked is uniformly distributed Uniform
companies is uniformly distributed Uniform
app_complete_flag is uniformly distributed Uniform
1_on_1_industry_mentorship is an unsupported type, check if it needs cleaning or further analysis Unsupported
career_coach is an unsupported type, check if it needs cleaning or further analysis Unsupported
chat_clicked is an unsupported type, check if it needs cleaning or further analysis Unsupported
download_syllabus is an unsupported type, check if it needs cleaning or further analysis Unsupported
homepage_upgrad_support_number_clicked is an unsupported type, check if it needs cleaning or further analysis Unsupported
industry_projects_case_studies is an unsupported type, check if it needs cleaning or further analysis Unsupported
payment_amount_toggle_mover is an unsupported type, check if it needs cleaning or further analysis Unsupported
program_structure is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2023-01-18 13:24:24.931567
Analysis finished2023-01-18 13:25:24.921443
Duration59.99 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

created_date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct234753
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
2021-12-14 04:55:05
 
10
2021-12-16 19:28:19
 
5
2021-12-06 06:48:43
 
5
2022-02-03 12:42:15
 
4
2021-08-08 14:28:39
 
4
Other values (234748)
239972 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters4560000
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique229720 ?
Unique (%)95.7%

Sample

1st row2021-08-13 10:44:09
2nd row2021-11-04 06:34:10
3rd row2021-07-29 14:01:00
4th row2021-12-01 13:21:54
5th row2021-12-10 07:22:51

Common Values

ValueCountFrequency (%)
2021-12-14 04:55:0510
 
< 0.1%
2021-12-16 19:28:195
 
< 0.1%
2021-12-06 06:48:435
 
< 0.1%
2022-02-03 12:42:154
 
< 0.1%
2021-08-08 14:28:394
 
< 0.1%
2022-01-20 08:46:254
 
< 0.1%
2022-01-05 09:34:364
 
< 0.1%
2021-08-08 15:51:554
 
< 0.1%
2022-01-17 10:31:444
 
< 0.1%
2021-12-29 16:31:014
 
< 0.1%
Other values (234743)239952
> 99.9%

Length

2023-01-18T13:25:25.494844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-08-101725
 
0.4%
2021-09-131581
 
0.3%
2021-11-161525
 
0.3%
2022-01-201520
 
0.3%
2022-01-031517
 
0.3%
2021-12-011507
 
0.3%
2021-11-291486
 
0.3%
2021-12-291484
 
0.3%
2022-01-041456
 
0.3%
2021-11-171449
 
0.3%
Other values (69354)464750
96.8%

Most occurring characters

ValueCountFrequency (%)
2825421
18.1%
1752578
16.5%
0741639
16.3%
-480000
10.5%
:480000
10.5%
240000
 
5.3%
3186539
 
4.1%
5184878
 
4.1%
4179779
 
3.9%
8129638
 
2.8%
Other values (3)359528
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3360000
73.7%
Dash Punctuation480000
 
10.5%
Other Punctuation480000
 
10.5%
Space Separator240000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2825421
24.6%
1752578
22.4%
0741639
22.1%
3186539
 
5.6%
5184878
 
5.5%
4179779
 
5.4%
8129638
 
3.9%
7129041
 
3.8%
9122945
 
3.7%
6107542
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
-480000
100.0%
Space Separator
ValueCountFrequency (%)
240000
100.0%
Other Punctuation
ValueCountFrequency (%)
:480000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4560000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2825421
18.1%
1752578
16.5%
0741639
16.3%
-480000
10.5%
:480000
10.5%
240000
 
5.3%
3186539
 
4.1%
5184878
 
4.1%
4179779
 
3.9%
8129638
 
2.8%
Other values (3)359528
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4560000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2825421
18.1%
1752578
16.5%
0741639
16.3%
-480000
10.5%
:480000
10.5%
240000
 
5.3%
3186539
 
4.1%
5184878
 
4.1%
4179779
 
3.9%
8129638
 
2.8%
Other values (3)359528
7.9%

city_mapped
Categorical

HIGH CARDINALITY
MISSING

Distinct4597
Distinct (%)2.0%
Missing9403
Missing (%)3.9%
Memory size3.7 MiB
ncr
35263 
mumbai
22186 
bengaluru
19530 
hyderabad
14376 
pune
14014 
Other values (4592)
125228 

Length

Max length61
Median length7
Mean length6.734281018
Min length1

Characters and Unicode

Total characters1552905
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2427 ?
Unique (%)1.1%

Sample

1st rowmumbai
2nd rowncr
3rd rowmeerut
4th rowncr
5th rowahmedabad

Common Values

ValueCountFrequency (%)
ncr35263
 
14.7%
mumbai22186
 
9.2%
bengaluru19530
 
8.1%
hyderabad14376
 
6.0%
pune14014
 
5.8%
chennai9697
 
4.0%
kolkata8340
 
3.5%
lucknow7073
 
2.9%
ahmedabad6101
 
2.5%
jaipur4417
 
1.8%
Other values (4587)89600
37.3%
(Missing)9403
 
3.9%

Length

2023-01-18T13:25:25.777750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ncr35265
 
15.0%
mumbai22193
 
9.4%
bengaluru19531
 
8.3%
hyderabad14376
 
6.1%
pune14021
 
6.0%
chennai10121
 
4.3%
kolkata8349
 
3.6%
lucknow7073
 
3.0%
ahmedabad6101
 
2.6%
jaipur4645
 
2.0%
Other values (4688)93479
39.8%

Most occurring characters

ValueCountFrequency (%)
a260306
16.8%
n143332
 
9.2%
r140618
 
9.1%
u127886
 
8.2%
e89195
 
5.7%
b83535
 
5.4%
i81499
 
5.2%
d73791
 
4.8%
m72086
 
4.6%
h71745
 
4.6%
Other values (17)408912
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1548348
99.7%
Space Separator4557
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a260306
16.8%
n143332
 
9.3%
r140618
 
9.1%
u127886
 
8.3%
e89195
 
5.8%
b83535
 
5.4%
i81499
 
5.3%
d73791
 
4.8%
m72086
 
4.7%
h71745
 
4.6%
Other values (16)404355
26.1%
Space Separator
ValueCountFrequency (%)
4557
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1548348
99.7%
Common4557
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a260306
16.8%
n143332
 
9.3%
r140618
 
9.1%
u127886
 
8.3%
e89195
 
5.8%
b83535
 
5.4%
i81499
 
5.3%
d73791
 
4.8%
m72086
 
4.7%
h71745
 
4.6%
Other values (16)404355
26.1%
Common
ValueCountFrequency (%)
4557
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1552905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a260306
16.8%
n143332
 
9.2%
r140618
 
9.1%
u127886
 
8.2%
e89195
 
5.7%
b83535
 
5.4%
i81499
 
5.2%
d73791
 
4.8%
m72086
 
4.6%
h71745
 
4.6%
Other values (17)408912
26.3%

first_platform_c
Categorical

HIGH CORRELATION

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
Level0
84532 
Level3
41028 
Level7
38267 
Level1
20832 
Level2
13284 
Other values (44)
42057 

Length

Max length7
Median length6
Mean length6.063625
Min length6

Characters and Unicode

Total characters1455270
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowLevel0
2nd rowLevel1
3rd rowLevel0
4th rowLevel1
5th rowLevel2

Common Values

ValueCountFrequency (%)
Level084532
35.2%
Level341028
17.1%
Level738267
15.9%
Level120832
 
8.7%
Level213284
 
5.5%
Level810838
 
4.5%
Level59768
 
4.1%
Level45514
 
2.3%
Level163790
 
1.6%
Level103288
 
1.4%
Other values (39)8859
 
3.7%

Length

2023-01-18T13:25:26.046837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
level084532
35.2%
level341028
17.1%
level738267
15.9%
level120832
 
8.7%
level213284
 
5.5%
level810838
 
4.5%
level59768
 
4.1%
level45514
 
2.3%
level163790
 
1.6%
level103288
 
1.4%
Other values (39)8859
 
3.7%

Most occurring characters

ValueCountFrequency (%)
e480000
33.0%
L240000
16.5%
v240000
16.5%
l240000
16.5%
087917
 
6.0%
341784
 
2.9%
738362
 
2.6%
136380
 
2.5%
216844
 
1.2%
812972
 
0.9%
Other values (4)21011
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter960000
66.0%
Decimal Number255270
 
17.5%
Uppercase Letter240000
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
087917
34.4%
341784
16.4%
738362
15.0%
136380
14.3%
216844
 
6.6%
812972
 
5.1%
59891
 
3.9%
46309
 
2.5%
64526
 
1.8%
9285
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e480000
50.0%
v240000
25.0%
l240000
25.0%
Uppercase Letter
ValueCountFrequency (%)
L240000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1200000
82.5%
Common255270
 
17.5%

Most frequent character per script

Common
ValueCountFrequency (%)
087917
34.4%
341784
16.4%
738362
15.0%
136380
14.3%
216844
 
6.6%
812972
 
5.1%
59891
 
3.9%
46309
 
2.5%
64526
 
1.8%
9285
 
0.1%
Latin
ValueCountFrequency (%)
e480000
40.0%
L240000
20.0%
v240000
20.0%
l240000
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1455270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e480000
33.0%
L240000
16.5%
v240000
16.5%
l240000
16.5%
087917
 
6.0%
341784
 
2.9%
738362
 
2.6%
136380
 
2.5%
216844
 
1.2%
812972
 
0.9%
Other values (4)21011
 
1.4%

first_utm_medium_c
Categorical

HIGH CORRELATION

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
Level0
54338 
Level2
27029 
Level6
23912 
Level3
20009 
Level4
18509 
Other values (40)
96203 

Length

Max length7
Median length6
Mean length6.257395833
Min length6

Characters and Unicode

Total characters1501775
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLevel0
2nd rowLevel1
3rd rowLevel2
4th rowLevel3
5th rowLevel2

Common Values

ValueCountFrequency (%)
Level054338
22.6%
Level227029
11.3%
Level623912
10.0%
Level320009
 
8.3%
Level418509
 
7.7%
Level915595
 
6.5%
Level119628
 
4.0%
Level59097
 
3.8%
Level87381
 
3.1%
Level204187
 
1.7%
Other values (35)50315
21.0%

Length

2023-01-18T13:25:26.306481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
level054338
22.6%
level227029
11.3%
level623912
10.0%
level320009
 
8.3%
level418509
 
7.7%
level915595
 
6.5%
level119628
 
4.0%
level59097
 
3.8%
level87381
 
3.1%
level204187
 
1.7%
Other values (35)50315
21.0%

Most occurring characters

ValueCountFrequency (%)
e480000
32.0%
L240000
16.0%
v240000
16.0%
l240000
16.0%
066127
 
4.4%
345361
 
3.0%
243948
 
2.9%
142378
 
2.8%
631235
 
2.1%
426575
 
1.8%
Other values (4)46151
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter960000
63.9%
Decimal Number301775
 
20.1%
Uppercase Letter240000
 
16.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
066127
21.9%
345361
15.0%
243948
14.6%
142378
14.0%
631235
10.4%
426575
8.8%
919569
 
6.5%
514132
 
4.7%
89223
 
3.1%
73227
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
e480000
50.0%
v240000
25.0%
l240000
25.0%
Uppercase Letter
ValueCountFrequency (%)
L240000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1200000
79.9%
Common301775
 
20.1%

Most frequent character per script

Common
ValueCountFrequency (%)
066127
21.9%
345361
15.0%
243948
14.6%
142378
14.0%
631235
10.4%
426575
8.8%
919569
 
6.5%
514132
 
4.7%
89223
 
3.1%
73227
 
1.1%
Latin
ValueCountFrequency (%)
e480000
40.0%
L240000
20.0%
v240000
20.0%
l240000
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1501775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e480000
32.0%
L240000
16.0%
v240000
16.0%
l240000
16.0%
066127
 
4.4%
345361
 
3.0%
243948
 
2.9%
142378
 
2.8%
631235
 
2.1%
426575
 
1.8%
Other values (4)46151
 
3.1%

first_utm_source_c
Categorical

HIGH CARDINALITY

Distinct142
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
Level2
83146 
Level0
56709 
Level7
18719 
Level4
16133 
Level6
15728 
Other values (137)
49565 

Length

Max length8
Median length6
Mean length6.1574625
Min length6

Characters and Unicode

Total characters1477791
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)< 0.1%

Sample

1st rowLevel0
2nd rowLevel1
3rd rowLevel2
4th rowLevel3
5th rowLevel2

Common Values

ValueCountFrequency (%)
Level283146
34.6%
Level056709
23.6%
Level718719
 
7.8%
Level416133
 
6.7%
Level615728
 
6.6%
Level1611081
 
4.6%
Level55517
 
2.3%
Level144856
 
2.0%
Level114415
 
1.8%
Level124154
 
1.7%
Other values (132)19542
 
8.1%

Length

2023-01-18T13:25:26.574573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
level283146
34.6%
level056709
23.6%
level718719
 
7.8%
level416133
 
6.7%
level615728
 
6.6%
level1611081
 
4.6%
level55517
 
2.3%
level144856
 
2.0%
level114415
 
1.8%
level124154
 
1.7%
Other values (132)19542
 
8.1%

Most occurring characters

ValueCountFrequency (%)
e480000
32.5%
L240000
16.2%
v240000
16.2%
l240000
16.2%
294419
 
6.4%
057225
 
3.9%
137251
 
2.5%
627517
 
1.9%
421857
 
1.5%
719210
 
1.3%
Other values (4)20312
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter960000
65.0%
Decimal Number277791
 
18.8%
Uppercase Letter240000
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
294419
34.0%
057225
20.6%
137251
 
13.4%
627517
 
9.9%
421857
 
7.9%
719210
 
6.9%
58368
 
3.0%
94702
 
1.7%
34025
 
1.4%
83217
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
e480000
50.0%
v240000
25.0%
l240000
25.0%
Uppercase Letter
ValueCountFrequency (%)
L240000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1200000
81.2%
Common277791
 
18.8%

Most frequent character per script

Common
ValueCountFrequency (%)
294419
34.0%
057225
20.6%
137251
 
13.4%
627517
 
9.9%
421857
 
7.9%
719210
 
6.9%
58368
 
3.0%
94702
 
1.7%
34025
 
1.4%
83217
 
1.2%
Latin
ValueCountFrequency (%)
e480000
40.0%
L240000
20.0%
v240000
20.0%
l240000
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1477791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e480000
32.5%
L240000
16.2%
v240000
16.2%
l240000
16.2%
294419
 
6.4%
057225
 
3.9%
137251
 
2.5%
627517
 
1.9%
421857
 
1.5%
719210
 
1.3%
Other values (4)20312
 
1.4%

total_leads_droppped
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)< 0.1%
Missing826
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.650810707
Minimum1
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-18T13:25:26.712105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum89
Range88
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.365227635
Coefficient of variation (CV)0.8270043496
Kurtosis406.0823408
Mean1.650810707
Median Absolute Deviation (MAD)0
Skewness12.56359153
Sum394831
Variance1.863846495
MonotonicityNot monotonic
2023-01-18T13:25:26.846238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1145967
60.8%
260640
25.3%
319854
 
8.3%
46881
 
2.9%
52817
 
1.2%
61267
 
0.5%
7631
 
0.3%
8361
 
0.2%
9203
 
0.1%
10120
 
0.1%
Other values (47)433
 
0.2%
(Missing)826
 
0.3%
ValueCountFrequency (%)
1145967
60.8%
260640
25.3%
319854
 
8.3%
46881
 
2.9%
52817
 
1.2%
61267
 
0.5%
7631
 
0.3%
8361
 
0.2%
9203
 
0.1%
10120
 
0.1%
ValueCountFrequency (%)
892
< 0.1%
711
< 0.1%
601
< 0.1%
581
< 0.1%
541
< 0.1%
531
< 0.1%
522
< 0.1%
512
< 0.1%
502
< 0.1%
482
< 0.1%

referred_lead
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing826
Missing (%)0.3%
Memory size3.7 MiB
0.0
231430 
1.0
 
7744

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters717522
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0231430
96.4%
1.07744
 
3.2%
(Missing)826
 
0.3%

Length

2023-01-18T13:25:27.054371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:27.121007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0231430
96.8%
1.07744
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0470604
65.6%
.239174
33.3%
17744
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number478348
66.7%
Other Punctuation239174
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0470604
98.4%
17744
 
1.6%
Other Punctuation
ValueCountFrequency (%)
.239174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common717522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0470604
65.6%
.239174
33.3%
17744
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII717522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0470604
65.6%
.239174
33.3%
17744
 
1.1%

1_on_1_industry_mentorship
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing240000
Missing (%)100.0%
Memory size3.7 MiB

call_us_button_clicked
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing239998
Missing (%)> 99.9%
Memory size3.7 MiB
2.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row2.0
2nd row1.0

Common Values

ValueCountFrequency (%)
2.01
 
< 0.1%
1.01
 
< 0.1%
(Missing)239998
> 99.9%

Length

2023-01-18T13:25:27.289019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:27.359341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.01
50.0%
1.01
50.0%

Most occurring characters

ValueCountFrequency (%)
.2
33.3%
02
33.3%
21
16.7%
11
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4
66.7%
Other Punctuation2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02
50.0%
21
25.0%
11
25.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.2
33.3%
02
33.3%
21
16.7%
11
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.2
33.3%
02
33.3%
21
16.7%
11
16.7%

career_assistance
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)75.0%
Missing239996
Missing (%)> 99.9%
Memory size3.7 MiB
1.0
2.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row1.0

Common Values

ValueCountFrequency (%)
1.02
 
< 0.1%
2.01
 
< 0.1%
3.01
 
< 0.1%
(Missing)239996
> 99.9%

Length

2023-01-18T13:25:27.531417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:27.602128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.02
50.0%
2.01
25.0%
3.01
25.0%

Most occurring characters

ValueCountFrequency (%)
.4
33.3%
04
33.3%
12
16.7%
21
 
8.3%
31
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8
66.7%
Other Punctuation4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
50.0%
12
25.0%
21
 
12.5%
31
 
12.5%
Other Punctuation
ValueCountFrequency (%)
.4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4
33.3%
04
33.3%
12
16.7%
21
 
8.3%
31
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4
33.3%
04
33.3%
12
16.7%
21
 
8.3%
31
 
8.3%

career_coach
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing240000
Missing (%)100.0%
Memory size3.7 MiB

career_impact
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)60.0%
Missing239995
Missing (%)> 99.9%
Memory size3.7 MiB
1.0
3.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)20.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.02
 
< 0.1%
3.02
 
< 0.1%
2.01
 
< 0.1%
(Missing)239995
> 99.9%

Length

2023-01-18T13:25:27.780319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:27.849383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.02
40.0%
3.02
40.0%
2.01
20.0%

Most occurring characters

ValueCountFrequency (%)
.5
33.3%
05
33.3%
12
 
13.3%
32
 
13.3%
21
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
66.7%
Other Punctuation5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05
50.0%
12
 
20.0%
32
 
20.0%
21
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.5
33.3%
05
33.3%
12
 
13.3%
32
 
13.3%
21
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.5
33.3%
05
33.3%
12
 
13.3%
32
 
13.3%
21
 
6.7%

careers
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)40.0%
Missing239980
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-18T13:25:27.912102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33.25
95-th percentile11.9
Maximum29
Range28
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation6.496962853
Coefficient of variation (CV)1.624240713
Kurtosis12.45407757
Mean4
Median Absolute Deviation (MAD)1
Skewness3.371355954
Sum80
Variance42.21052632
MonotonicityNot monotonic
2023-01-18T13:25:28.001948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
19
 
< 0.1%
25
 
< 0.1%
31
 
< 0.1%
291
 
< 0.1%
91
 
< 0.1%
111
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
(Missing)239980
> 99.9%
ValueCountFrequency (%)
19
< 0.1%
25
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
91
 
< 0.1%
111
 
< 0.1%
291
 
< 0.1%
ValueCountFrequency (%)
291
 
< 0.1%
111
 
< 0.1%
91
 
< 0.1%
51
 
< 0.1%
41
 
< 0.1%
31
 
< 0.1%
25
< 0.1%
19
< 0.1%

chat_clicked
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing240000
Missing (%)100.0%
Memory size3.7 MiB

companies
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing239998
Missing (%)> 99.9%
Memory size3.7 MiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row1.0
2nd row2.0

Common Values

ValueCountFrequency (%)
1.01
 
< 0.1%
2.01
 
< 0.1%
(Missing)239998
> 99.9%

Length

2023-01-18T13:25:28.209349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:28.281086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.01
50.0%
2.01
50.0%

Most occurring characters

ValueCountFrequency (%)
.2
33.3%
02
33.3%
11
16.7%
21
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4
66.7%
Other Punctuation2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02
50.0%
11
25.0%
21
25.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.2
33.3%
02
33.3%
11
16.7%
21
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.2
33.3%
02
33.3%
11
16.7%
21
16.7%

download_button_clicked
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)29.0%
Missing239893
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean10.55140187
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-18T13:25:28.356830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37.5
95-th percentile46.8
Maximum98
Range97
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation18.65588766
Coefficient of variation (CV)1.768095641
Kurtosis9.051424841
Mean10.55140187
Median Absolute Deviation (MAD)2
Skewness2.873550338
Sum1129
Variance348.0421442
MonotonicityNot monotonic
2023-01-18T13:25:28.476633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
131
 
< 0.1%
222
 
< 0.1%
310
 
< 0.1%
48
 
< 0.1%
73
 
< 0.1%
53
 
< 0.1%
63
 
< 0.1%
82
 
< 0.1%
362
 
< 0.1%
572
 
< 0.1%
Other values (21)21
 
< 0.1%
(Missing)239893
> 99.9%
ValueCountFrequency (%)
131
< 0.1%
222
< 0.1%
310
 
< 0.1%
48
 
< 0.1%
53
 
< 0.1%
63
 
< 0.1%
73
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
981
< 0.1%
971
< 0.1%
751
< 0.1%
572
< 0.1%
481
< 0.1%
441
< 0.1%
401
< 0.1%
362
< 0.1%
341
< 0.1%
331
< 0.1%

download_syllabus
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing240000
Missing (%)100.0%
Memory size3.7 MiB

emi_partner_click
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct39
Distinct (%)49.4%
Missing239921
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean27.86075949
Minimum1
Maximum319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-18T13:25:28.600272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.9
Q14
median9
Q327
95-th percentile122.1
Maximum319
Range318
Interquartile range (IQR)23

Descriptive statistics

Standard deviation48.4981454
Coefficient of variation (CV)1.74073307
Kurtosis16.75134965
Mean27.86075949
Median Absolute Deviation (MAD)7
Skewness3.58802169
Sum2201
Variance2352.070107
MonotonicityNot monotonic
2023-01-18T13:25:28.722448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
29
 
< 0.1%
67
 
< 0.1%
55
 
< 0.1%
45
 
< 0.1%
115
 
< 0.1%
84
 
< 0.1%
14
 
< 0.1%
33
 
< 0.1%
133
 
< 0.1%
172
 
< 0.1%
Other values (29)32
 
< 0.1%
(Missing)239921
> 99.9%
ValueCountFrequency (%)
14
< 0.1%
29
< 0.1%
33
 
< 0.1%
45
< 0.1%
55
< 0.1%
67
< 0.1%
72
 
< 0.1%
84
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
3191
< 0.1%
1641
< 0.1%
1331
< 0.1%
1231
< 0.1%
1221
< 0.1%
1081
< 0.1%
1041
< 0.1%
971
< 0.1%
831
< 0.1%
741
< 0.1%

emi_plans_clicked
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)55.7%
Missing239939
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean42.72131148
Minimum1
Maximum246
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-18T13:25:28.846606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median10
Q366
95-th percentile204
Maximum246
Range245
Interquartile range (IQR)64

Descriptive statistics

Standard deviation63.34880455
Coefficient of variation (CV)1.48283848
Kurtosis3.218107336
Mean42.72131148
Median Absolute Deviation (MAD)9
Skewness1.934837832
Sum2606
Variance4013.071038
MonotonicityNot monotonic
2023-01-18T13:25:28.968200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
111
 
< 0.1%
27
 
< 0.1%
34
 
< 0.1%
54
 
< 0.1%
522
 
< 0.1%
662
 
< 0.1%
252
 
< 0.1%
212
 
< 0.1%
102
 
< 0.1%
711
 
< 0.1%
Other values (24)24
 
< 0.1%
(Missing)239939
> 99.9%
ValueCountFrequency (%)
111
< 0.1%
27
< 0.1%
34
 
< 0.1%
41
 
< 0.1%
54
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
102
 
< 0.1%
111
 
< 0.1%
151
 
< 0.1%
ValueCountFrequency (%)
2461
< 0.1%
2401
< 0.1%
2321
< 0.1%
2041
< 0.1%
1521
< 0.1%
1331
< 0.1%
1241
< 0.1%
1101
< 0.1%
1071
< 0.1%
1001
< 0.1%

fee_component_click
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)60.0%
Missing239995
Missing (%)> 99.9%
Memory size3.7 MiB
1.0
10.0
2.0

Length

Max length4
Median length3
Mean length3.2
Min length3

Characters and Unicode

Total characters16
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)40.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row10.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.03
 
< 0.1%
10.01
 
< 0.1%
2.01
 
< 0.1%
(Missing)239995
> 99.9%

Length

2023-01-18T13:25:29.208435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:29.282448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.03
60.0%
10.01
 
20.0%
2.01
 
20.0%

Most occurring characters

ValueCountFrequency (%)
06
37.5%
.5
31.2%
14
25.0%
21
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11
68.8%
Other Punctuation5
31.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06
54.5%
14
36.4%
21
 
9.1%
Other Punctuation
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06
37.5%
.5
31.2%
14
25.0%
21
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06
37.5%
.5
31.2%
14
25.0%
21
 
6.2%

hiring_partners
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)22.2%
Missing239991
Missing (%)> 99.9%
Memory size3.7 MiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters27
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.05
 
< 0.1%
2.04
 
< 0.1%
(Missing)239991
> 99.9%

Length

2023-01-18T13:25:29.450686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:29.515943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.05
55.6%
2.04
44.4%

Most occurring characters

ValueCountFrequency (%)
.9
33.3%
09
33.3%
15
18.5%
24
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18
66.7%
Other Punctuation9
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09
50.0%
15
27.8%
24
22.2%
Other Punctuation
ValueCountFrequency (%)
.9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common27
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.9
33.3%
09
33.3%
15
18.5%
24
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII27
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.9
33.3%
09
33.3%
15
18.5%
24
14.8%

homepage_upgrad_support_number_clicked
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing240000
Missing (%)100.0%
Memory size3.7 MiB

industry_projects_case_studies
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing240000
Missing (%)100.0%
Memory size3.7 MiB

live_chat_button_clicked
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)22.2%
Missing239982
Missing (%)> 99.9%
Memory size3.7 MiB
1.0
10 
2.0
3.0
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row4.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.010
 
< 0.1%
2.03
 
< 0.1%
3.03
 
< 0.1%
4.02
 
< 0.1%
(Missing)239982
> 99.9%

Length

2023-01-18T13:25:29.695534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:29.767428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.010
55.6%
2.03
 
16.7%
3.03
 
16.7%
4.02
 
11.1%

Most occurring characters

ValueCountFrequency (%)
.18
33.3%
018
33.3%
110
18.5%
23
 
5.6%
33
 
5.6%
42
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36
66.7%
Other Punctuation18
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018
50.0%
110
27.8%
23
 
8.3%
33
 
8.3%
42
 
5.6%
Other Punctuation
ValueCountFrequency (%)
.18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common54
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.18
33.3%
018
33.3%
110
18.5%
23
 
5.6%
33
 
5.6%
42
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.18
33.3%
018
33.3%
110
18.5%
23
 
5.6%
33
 
5.6%
42
 
3.7%

payment_amount_toggle_mover
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing240000
Missing (%)100.0%
Memory size3.7 MiB

placement_support
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)66.7%
Missing239997
Missing (%)> 99.9%
Memory size3.7 MiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st row1.0
2nd row2.0
3rd row1.0

Common Values

ValueCountFrequency (%)
1.02
 
< 0.1%
2.01
 
< 0.1%
(Missing)239997
> 99.9%

Length

2023-01-18T13:25:29.939184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:30.009061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.02
66.7%
2.01
33.3%

Most occurring characters

ValueCountFrequency (%)
.3
33.3%
03
33.3%
12
22.2%
21
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6
66.7%
Other Punctuation3
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03
50.0%
12
33.3%
21
 
16.7%
Other Punctuation
ValueCountFrequency (%)
.3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.3
33.3%
03
33.3%
12
22.2%
21
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.3
33.3%
03
33.3%
12
22.2%
21
 
11.1%

placement_support_banner_tab_clicked
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing239999
Missing (%)> 99.9%
Memory size3.7 MiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.01
 
< 0.1%
(Missing)239999
> 99.9%

Length

2023-01-18T13:25:30.169016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:30.234423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.01
100.0%

Most occurring characters

ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2
66.7%
Other Punctuation1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11
50.0%
01
50.0%
Other Punctuation
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%

program_structure
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing240000
Missing (%)100.0%
Memory size3.7 MiB

programme_curriculum
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing239999
Missing (%)> 99.9%
Memory size3.7 MiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.01
 
< 0.1%
(Missing)239999
> 99.9%

Length

2023-01-18T13:25:30.385123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:30.450830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.01
100.0%

Most occurring characters

ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2
66.7%
Other Punctuation1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11
50.0%
01
50.0%
Other Punctuation
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%

programme_faculty
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing239999
Missing (%)> 99.9%
Memory size3.7 MiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.01
 
< 0.1%
(Missing)239999
> 99.9%

Length

2023-01-18T13:25:30.603642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:30.668839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.01
100.0%

Most occurring characters

ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2
66.7%
Other Punctuation1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11
50.0%
01
50.0%
Other Punctuation
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%
Distinct1
Distinct (%)50.0%
Missing239998
Missing (%)> 99.9%
Memory size3.7 MiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.02
 
< 0.1%
(Missing)239998
> 99.9%

Length

2023-01-18T13:25:30.822596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:30.891764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.02
100.0%

Most occurring characters

ValueCountFrequency (%)
12
33.3%
.2
33.3%
02
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4
66.7%
Other Punctuation2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12
50.0%
02
50.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12
33.3%
.2
33.3%
02
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
33.3%
.2
33.3%
02
33.3%

shorts_entry_click
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)30.8%
Missing239987
Missing (%)> 99.9%
Memory size3.7 MiB
1.0
10 
32.0
 
1
9.0
 
1
4.0
 
1

Length

Max length4
Median length3
Mean length3.076923077
Min length3

Characters and Unicode

Total characters40
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)23.1%

Sample

1st row32.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.010
 
< 0.1%
32.01
 
< 0.1%
9.01
 
< 0.1%
4.01
 
< 0.1%
(Missing)239987
> 99.9%

Length

2023-01-18T13:25:31.049623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:31.121512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.010
76.9%
32.01
 
7.7%
9.01
 
7.7%
4.01
 
7.7%

Most occurring characters

ValueCountFrequency (%)
.13
32.5%
013
32.5%
110
25.0%
31
 
2.5%
21
 
2.5%
91
 
2.5%
41
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27
67.5%
Other Punctuation13
32.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013
48.1%
110
37.0%
31
 
3.7%
21
 
3.7%
91
 
3.7%
41
 
3.7%
Other Punctuation
ValueCountFrequency (%)
.13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common40
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.13
32.5%
013
32.5%
110
25.0%
31
 
2.5%
21
 
2.5%
91
 
2.5%
41
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII40
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.13
32.5%
013
32.5%
110
25.0%
31
 
2.5%
21
 
2.5%
91
 
2.5%
41
 
2.5%

social_referral_click
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)80.0%
Missing239995
Missing (%)> 99.9%
Memory size3.7 MiB
1.0
3.0
2.0
8.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)60.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row8.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.02
 
< 0.1%
3.01
 
< 0.1%
2.01
 
< 0.1%
8.01
 
< 0.1%
(Missing)239995
> 99.9%

Length

2023-01-18T13:25:31.686151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:31.755642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.02
40.0%
3.01
20.0%
2.01
20.0%
8.01
20.0%

Most occurring characters

ValueCountFrequency (%)
.5
33.3%
05
33.3%
12
 
13.3%
31
 
6.7%
21
 
6.7%
81
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
66.7%
Other Punctuation5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05
50.0%
12
 
20.0%
31
 
10.0%
21
 
10.0%
81
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.5
33.3%
05
33.3%
12
 
13.3%
31
 
6.7%
21
 
6.7%
81
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.5
33.3%
05
33.3%
12
 
13.3%
31
 
6.7%
21
 
6.7%
81
 
6.7%

specialisation_tab_clicked
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)70.5%
Missing239956
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean36.29545455
Minimum1
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-18T13:25:31.840132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14.75
median12
Q358.75
95-th percentile110.95
Maximum196
Range195
Interquartile range (IQR)54

Descriptive statistics

Standard deviation45.44742915
Coefficient of variation (CV)1.252152087
Kurtosis2.69263448
Mean36.29545455
Median Absolute Deviation (MAD)11
Skewness1.666444767
Sum1597
Variance2065.468816
MonotonicityNot monotonic
2023-01-18T13:25:31.958990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
15
 
< 0.1%
83
 
< 0.1%
93
 
< 0.1%
43
 
< 0.1%
132
 
< 0.1%
62
 
< 0.1%
22
 
< 0.1%
881
 
< 0.1%
111
 
< 0.1%
981
 
< 0.1%
Other values (21)21
 
< 0.1%
(Missing)239956
> 99.9%
ValueCountFrequency (%)
15
< 0.1%
22
 
< 0.1%
31
 
< 0.1%
43
< 0.1%
51
 
< 0.1%
62
 
< 0.1%
71
 
< 0.1%
83
< 0.1%
93
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
1961
< 0.1%
1501
< 0.1%
1121
< 0.1%
1051
< 0.1%
1021
< 0.1%
981
< 0.1%
881
< 0.1%
801
< 0.1%
751
< 0.1%
651
< 0.1%

specializations
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)75.0%
Missing239996
Missing (%)> 99.9%
Memory size3.7 MiB
1.0
3.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row1.0
2nd row3.0
3rd row2.0
4th row1.0

Common Values

ValueCountFrequency (%)
1.02
 
< 0.1%
3.01
 
< 0.1%
2.01
 
< 0.1%
(Missing)239996
> 99.9%

Length

2023-01-18T13:25:32.183916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:32.254426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.02
50.0%
3.01
25.0%
2.01
25.0%

Most occurring characters

ValueCountFrequency (%)
.4
33.3%
04
33.3%
12
16.7%
31
 
8.3%
21
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8
66.7%
Other Punctuation4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04
50.0%
12
25.0%
31
 
12.5%
21
 
12.5%
Other Punctuation
ValueCountFrequency (%)
.4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4
33.3%
04
33.3%
12
16.7%
31
 
8.3%
21
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4
33.3%
04
33.3%
12
16.7%
31
 
8.3%
21
 
8.3%

specilization_click
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)85.7%
Missing239993
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean5.285714286
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-18T13:25:32.310546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median5
Q37.5
95-th percentile11.8
Maximum13
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.498677054
Coefficient of variation (CV)0.8511010643
Kurtosis-0.2462217301
Mean5.285714286
Median Absolute Deviation (MAD)4
Skewness0.8143570582
Sum37
Variance20.23809524
MonotonicityNot monotonic
2023-01-18T13:25:32.401012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
12
 
< 0.1%
61
 
< 0.1%
51
 
< 0.1%
21
 
< 0.1%
91
 
< 0.1%
131
 
< 0.1%
(Missing)239993
> 99.9%
ValueCountFrequency (%)
12
< 0.1%
21
< 0.1%
51
< 0.1%
61
< 0.1%
91
< 0.1%
131
< 0.1%
ValueCountFrequency (%)
131
< 0.1%
91
< 0.1%
61
< 0.1%
51
< 0.1%
21
< 0.1%
12
< 0.1%

syllabus
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct10
Distinct (%)34.5%
Missing239971
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean7.068965517
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-18T13:25:32.492926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile37
Maximum50
Range49
Interquartile range (IQR)3

Descriptive statistics

Standard deviation12.45257011
Coefficient of variation (CV)1.761583089
Kurtosis5.839513432
Mean7.068965517
Median Absolute Deviation (MAD)1
Skewness2.562720807
Sum205
Variance155.0665025
MonotonicityNot monotonic
2023-01-18T13:25:32.588437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
110
 
< 0.1%
27
 
< 0.1%
44
 
< 0.1%
32
 
< 0.1%
341
 
< 0.1%
171
 
< 0.1%
61
 
< 0.1%
131
 
< 0.1%
501
 
< 0.1%
391
 
< 0.1%
(Missing)239971
> 99.9%
ValueCountFrequency (%)
110
< 0.1%
27
< 0.1%
32
 
< 0.1%
44
 
< 0.1%
61
 
< 0.1%
131
 
< 0.1%
171
 
< 0.1%
341
 
< 0.1%
391
 
< 0.1%
501
 
< 0.1%
ValueCountFrequency (%)
501
 
< 0.1%
391
 
< 0.1%
341
 
< 0.1%
171
 
< 0.1%
131
 
< 0.1%
61
 
< 0.1%
44
 
< 0.1%
32
 
< 0.1%
27
< 0.1%
110
< 0.1%

syllabus_expand
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct49
Distinct (%)52.1%
Missing239906
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean83.44680851
Minimum1
Maximum930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-18T13:25:32.710605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q371.5
95-th percentile390.85
Maximum930
Range929
Interquartile range (IQR)69.5

Descriptive statistics

Standard deviation188.4179532
Coefficient of variation (CV)2.257940796
Kurtosis11.97359139
Mean83.44680851
Median Absolute Deviation (MAD)5
Skewness3.420265609
Sum7844
Variance35501.3251
MonotonicityNot monotonic
2023-01-18T13:25:32.848213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
215
 
< 0.1%
111
 
< 0.1%
47
 
< 0.1%
36
 
< 0.1%
65
 
< 0.1%
54
 
< 0.1%
83
 
< 0.1%
112
 
< 0.1%
421
 
< 0.1%
721
 
< 0.1%
Other values (39)39
 
< 0.1%
(Missing)239906
> 99.9%
ValueCountFrequency (%)
111
< 0.1%
215
< 0.1%
36
 
< 0.1%
47
< 0.1%
54
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
83
 
< 0.1%
101
 
< 0.1%
112
 
< 0.1%
ValueCountFrequency (%)
9301
< 0.1%
9281
< 0.1%
8871
< 0.1%
7391
< 0.1%
4501
< 0.1%
3591
< 0.1%
3091
< 0.1%
2751
< 0.1%
2541
< 0.1%
2421
< 0.1%

syllabus_submodule_expand
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)50.0%
Missing239958
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean39.71428571
Minimum1
Maximum457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-18T13:25:32.964822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.25
median5
Q321
95-th percentile179.5
Maximum457
Range456
Interquartile range (IQR)19.75

Descriptive statistics

Standard deviation92.79173789
Coefficient of variation (CV)2.336482609
Kurtosis12.41907508
Mean39.71428571
Median Absolute Deviation (MAD)4
Skewness3.458610702
Sum1668
Variance8610.30662
MonotonicityNot monotonic
2023-01-18T13:25:33.067396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
111
 
< 0.1%
54
 
< 0.1%
23
 
< 0.1%
33
 
< 0.1%
142
 
< 0.1%
212
 
< 0.1%
42
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
561
 
< 0.1%
Other values (11)11
 
< 0.1%
(Missing)239958
> 99.9%
ValueCountFrequency (%)
111
< 0.1%
23
 
< 0.1%
33
 
< 0.1%
42
 
< 0.1%
54
 
< 0.1%
71
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
142
 
< 0.1%
201
 
< 0.1%
ValueCountFrequency (%)
4571
< 0.1%
3571
< 0.1%
1811
< 0.1%
1511
< 0.1%
1181
< 0.1%
601
< 0.1%
561
< 0.1%
521
< 0.1%
381
< 0.1%
221
< 0.1%

tab_career_assistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)36.0%
Missing239975
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean7.84
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-18T13:25:33.161158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q37
95-th percentile26.4
Maximum57
Range56
Interquartile range (IQR)5

Descriptive statistics

Standard deviation12.85392288
Coefficient of variation (CV)1.639530979
Kurtosis8.616928893
Mean7.84
Median Absolute Deviation (MAD)1
Skewness2.783657263
Sum196
Variance165.2233333
MonotonicityNot monotonic
2023-01-18T13:25:33.246631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
29
 
< 0.1%
16
 
< 0.1%
43
 
< 0.1%
242
 
< 0.1%
571
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
131
 
< 0.1%
271
 
< 0.1%
(Missing)239975
> 99.9%
ValueCountFrequency (%)
16
< 0.1%
29
< 0.1%
43
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
131
 
< 0.1%
242
 
< 0.1%
271
 
< 0.1%
571
 
< 0.1%
ValueCountFrequency (%)
571
 
< 0.1%
271
 
< 0.1%
242
 
< 0.1%
131
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
43
 
< 0.1%
29
< 0.1%
16
< 0.1%

tab_job_opportunities
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)47.4%
Missing239981
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4.578947368
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-18T13:25:33.336828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q38
95-th percentile12.2
Maximum14
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.694154633
Coefficient of variation (CV)1.025160207
Kurtosis-0.7022014451
Mean4.578947368
Median Absolute Deviation (MAD)1
Skewness0.9955343554
Sum87
Variance22.03508772
MonotonicityNot monotonic
2023-01-18T13:25:33.432637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
18
 
< 0.1%
23
 
< 0.1%
122
 
< 0.1%
141
 
< 0.1%
31
 
< 0.1%
111
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
101
 
< 0.1%
(Missing)239981
> 99.9%
ValueCountFrequency (%)
18
< 0.1%
23
 
< 0.1%
31
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
122
 
< 0.1%
141
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
122
 
< 0.1%
111
 
< 0.1%
101
 
< 0.1%
61
 
< 0.1%
51
 
< 0.1%
31
 
< 0.1%
23
 
< 0.1%
18
< 0.1%

tab_student_support
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)62.5%
Missing239992
Missing (%)> 99.9%
Memory size3.7 MiB
1.0
2.0
6.0
3.0
8.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)37.5%

Sample

1st row1.0
2nd row6.0
3rd row1.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.03
 
< 0.1%
2.02
 
< 0.1%
6.01
 
< 0.1%
3.01
 
< 0.1%
8.01
 
< 0.1%
(Missing)239992
> 99.9%

Length

2023-01-18T13:25:33.653604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:33.726887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.03
37.5%
2.02
25.0%
6.01
 
12.5%
3.01
 
12.5%
8.01
 
12.5%

Most occurring characters

ValueCountFrequency (%)
.8
33.3%
08
33.3%
13
 
12.5%
22
 
8.3%
61
 
4.2%
31
 
4.2%
81
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16
66.7%
Other Punctuation8
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08
50.0%
13
 
18.8%
22
 
12.5%
61
 
6.2%
31
 
6.2%
81
 
6.2%
Other Punctuation
ValueCountFrequency (%)
.8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.8
33.3%
08
33.3%
13
 
12.5%
22
 
8.3%
61
 
4.2%
31
 
4.2%
81
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.8
33.3%
08
33.3%
13
 
12.5%
22
 
8.3%
61
 
4.2%
31
 
4.2%
81
 
4.2%

view_programs_page
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)41.7%
Missing239988
Missing (%)> 99.9%
Memory size3.7 MiB
2.0
3.0
1.0
4.0
11.0

Length

Max length4
Median length3
Mean length3.083333333
Min length3

Characters and Unicode

Total characters37
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)16.7%

Sample

1st row4.0
2nd row3.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.04
 
< 0.1%
3.03
 
< 0.1%
1.03
 
< 0.1%
4.01
 
< 0.1%
11.01
 
< 0.1%
(Missing)239988
> 99.9%

Length

2023-01-18T13:25:33.919541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:33.992690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.04
33.3%
3.03
25.0%
1.03
25.0%
4.01
 
8.3%
11.01
 
8.3%

Most occurring characters

ValueCountFrequency (%)
.12
32.4%
012
32.4%
15
13.5%
24
 
10.8%
33
 
8.1%
41
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25
67.6%
Other Punctuation12
32.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
012
48.0%
15
20.0%
24
 
16.0%
33
 
12.0%
41
 
4.0%
Other Punctuation
ValueCountFrequency (%)
.12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common37
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.12
32.4%
012
32.4%
15
13.5%
24
 
10.8%
33
 
8.1%
41
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII37
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.12
32.4%
012
32.4%
15
13.5%
24
 
10.8%
33
 
8.1%
41
 
2.7%

whatsapp_chat_click
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)20.0%
Missing239975
Missing (%)> 99.9%
Memory size3.7 MiB
1.0
15 
2.0
3.0
16.0
 
1
13.0
 
1

Length

Max length4
Median length3
Mean length3.08
Min length3

Characters and Unicode

Total characters77
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)8.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.015
 
< 0.1%
2.05
 
< 0.1%
3.03
 
< 0.1%
16.01
 
< 0.1%
13.01
 
< 0.1%
(Missing)239975
> 99.9%

Length

2023-01-18T13:25:34.183063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:34.255356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.015
60.0%
2.05
 
20.0%
3.03
 
12.0%
16.01
 
4.0%
13.01
 
4.0%

Most occurring characters

ValueCountFrequency (%)
.25
32.5%
025
32.5%
117
22.1%
25
 
6.5%
34
 
5.2%
61
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number52
67.5%
Other Punctuation25
32.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025
48.1%
117
32.7%
25
 
9.6%
34
 
7.7%
61
 
1.9%
Other Punctuation
ValueCountFrequency (%)
.25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common77
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.25
32.5%
025
32.5%
117
22.1%
25
 
6.5%
34
 
5.2%
61
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII77
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.25
32.5%
025
32.5%
117
22.1%
25
 
6.5%
34
 
5.2%
61
 
1.3%

app_complete_flag
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
0
120000 
1
120000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters240000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0120000
50.0%
1120000
50.0%

Length

2023-01-18T13:25:34.440380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-01-18T13:25:34.507389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0120000
50.0%
1120000
50.0%

Most occurring characters

ValueCountFrequency (%)
0120000
50.0%
1120000
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number240000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0120000
50.0%
1120000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common240000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0120000
50.0%
1120000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0120000
50.0%
1120000
50.0%

Interactions

2023-01-18T13:25:03.534308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:03.652462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:03.749626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:03.846917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:03.944411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:04.040478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:04.147314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:04.239352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:04.332974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:04.426604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:04.519197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:04.614787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:04.711670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:04.807537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:04.900555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:04.994700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:05.094301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:05.187813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:05.290440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:05.380785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:05.476323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:05.571707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:05.665829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:05.763433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:05.862904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:05.964651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:06.059208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:06.155601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:06.251890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:06.344887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:06.679321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:06.780323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:06.873672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:06.962973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:07.051203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:07.140853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:07.233965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:07.334040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:07.438585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:07.535258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:07.636294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:07.734917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:07.836134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:07.924285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:08.015867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:08.107059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:08.198911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:08.291979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:08.386689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:08.478668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:08.569689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:08.660110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:08.753548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:08.850300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:08.950019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:09.049140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:09.136828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:09.224887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:09.316037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:09.409206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:09.503466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:09.607736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:09.710408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:09.813381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:09.915746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:10.014051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:10.303432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:10.404778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:10.503401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:10.604997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:10.707695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:10.810277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:10.913693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:11.003740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:11.087754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:11.188533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:11.277723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:11.378740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:11.474140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:11.562944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:11.658700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:11.759362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:11.866293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:11.955730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:12.045203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:12.137623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:12.227572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:12.317486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:12.408369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:12.496789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:12.592404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:12.690854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:12.780443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:12.867589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:12.954657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:13.042434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:13.145719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:13.238330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:13.325846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:13.413352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:13.501869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:13.587679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:13.680190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:13.777428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:13.860271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:13.947791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:14.033378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:14.118733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:14.205548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:14.295354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:14.385414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:14.699660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:14.790806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:14.877644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:14.971092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:15.077994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:15.165869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:15.249583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:15.336169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:15.423154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:15.512236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:15.606370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:15.699092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:15.791614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:15.884158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:15.974403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:16.070756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:16.157633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:16.250054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:16.342207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:16.433461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:16.524981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:16.616387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:16.710680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:16.805473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:16.898469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:16.994651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:17.090397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:17.190768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:17.277263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:17.378902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:17.473862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:17.570705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-18T13:25:17.668728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-18T13:25:34.623165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-18T13:25:35.035158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-18T13:25:35.453840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-18T13:25:35.859348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-18T13:25:18.098614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-18T13:25:20.161129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-18T13:25:23.328212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-01-18T13:25:24.452276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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